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Record W4381140963 · doi:10.3126/scholars.v5i1.55809

The Learning Practices of the Students with Physical Disabilities

2022· article· en· W4381140963 on OpenAlexaff
Bal Dev Bhatt

Bibliographic record

VenueScholars Journal · 2022
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsWestern University
Fundersnot available
KeywordsNonprobability samplingPsychologyData collectionScheduleQualitative researchMathematics educationCase study researchLearning disabilityQualitative propertyPedagogyMedical educationDevelopmental psychologyMedicineEngineeringPopulation

Abstract

fetched live from OpenAlex

In this research article the current learning practices to the students with physical disabilities are discussed. It is a qualitative study. The main objective of the study is to analyze current learning practices used for students with physical disabilities in Kailali district. Interview schedule and classed observation were the major tools for the collection of data in the study. The researcher chose case study as a research design. In this study, the organization is a case where physically disabled students stay and study. Among eight teachers three teachers were selected through Purposive sampling method. The data were collected from teachers who were teaching physically disabled students in school teaching. The findings of the study showed that the practice of teaching physically disabled students seemed to be difficult and challenging. The foremost reason behind this is the lack of disability-assistive infrastructure, parental support, social stigma, teachers training and awareness. Moreover, the findings revealed that teachers’ professionalism, engagement with students, planning for the lesson, inculcating the spirit of a creative and critical thinking and setting goals can prove instrumental to maximize students’ outcomes. The implication of the study is that the teachers should enhance special skills to deal with physically disabled students for their better performance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.110
GPT teacher head0.556
Teacher spread0.447 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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